Statistical Model

Intrusion detection systems (IDSs) attempt to identify attacks by comparing collected data to predefined signatures known to be malicious (misuse Anomaly-based approaches have the advantage of being able to detect previously unknown attacks, but they suffer from the difficulty of building robust models of acceptable propose a new design of an anomaly Intrusion considered in our 3 main stages: normal behavior construction, anomaly detection and mixture model is used for behavior modeling from reference data. The associated Bayesian classification leads to the detection algorithm. A continuous model parameter re Real-time requirements are presented as well as detection and upgrade algorithms for the special case of Gaussian parametrical model.